new_my_likes
Mix the brand new and outdated knowledge:
deduped_my_likes
And, lastly, save the up to date knowledge by overwriting the outdated file:
rio::export(deduped_my_likes, 'my_likes.parquet')
Step 4. View and search your knowledge the standard manner
I wish to create a model of this knowledge particularly to make use of in a searchable desk. It features a hyperlink on the finish of every publish’s textual content to the unique publish on Bluesky, letting me simply view any pictures, replies, mother and father, or threads that aren’t in a publish’s plain textual content. I additionally take away some columns I don’t want within the desk.
my_likes_for_table
mutate(
Put up = str_glue("{Put up} >>"),
ExternalURL = ifelse(!is.na(ExternalURL), str_glue("{substr(ExternalURL, 1, 25)}..."), "")
) |>
choose(Put up, Identify, CreatedAt, ExternalURL)
Right here’s one approach to create a searchable HTML desk of that knowledge, utilizing the DT bundle:
DT::datatable(my_likes_for_table, rownames = FALSE, filter="high", escape = FALSE, choices = checklist(pageLength = 25, autoWidth = TRUE, filter = "high", lengthMenu = c(25, 50, 75, 100), searchHighlight = TRUE,
search = checklist(regex = TRUE)
)
)
This desk has a table-wide search field on the high proper and search filters for every column, so I can seek for two phrases in my desk, such because the #rstats hashtag in the primary search bar after which any publish the place the textual content accommodates LLM (the desk’s search isn’t case delicate) within the Put up column filter bar. Or, as a result of I enabled common expression looking out with the search = checklist(regex = TRUE) choice, I may use a single regexp lookahead sample (?=.rstats)(?=.(LLM)) within the search field.
IDG
Generative AI chatbots like ChatGPT and Claude might be fairly good at writing complicated common expressions. And with matching textual content highlights turned on within the desk, it will likely be simple so that you can see whether or not the regexp is doing what you need.
Question your Bluesky likes with an LLM
The best free manner to make use of generative AI to question these posts is by importing the information file to a service of your alternative. I’ve had good outcomes with Google’s NotebookLM, which is free and exhibits you the supply textual content for its solutions. NotebookLM has a beneficiant file restrict of 500,000 phrases or 200MB per supply, and Google says it gained’t practice its large language models (LLMs) in your knowledge.
The question “Somebody talked about an R bundle with science-related shade palettes” pulled up the precise publish I used to be considering of — one which I had favored after which re-posted with my very own feedback. And I didn’t have to present NotebookLLM my very own prompts or directions to inform it that I wished to 1) use solely that doc for solutions, and a pair of) see the supply textual content it used to generate its response. All I needed to do was ask my query.
IDG
I formatted the information to be a bit extra helpful and fewer wasteful by limiting CreatedAt to dates with out occasions, holding the publish URL as a separate column (as a substitute of a clickable hyperlink with added HTML), and deleting the exterior URLs column. I saved that slimmer model as a .txt and never .csv file, since NotebookLM doesn’t deal with .csv extentions.
my_likes_for_ai
mutate(CreatedAt = substr(CreatedAt, 1, 10)) |>
choose(Put up, Identify, CreatedAt, URL)
rio::export(my_likes_for_ai, "my_likes_for_ai.txt")
After importing your likes file to NotebookLM, you possibly can ask questions instantly as soon as the file is processed.
IDG
In the event you actually wished to question the doc inside R as a substitute of utilizing an exterior service, one choice is the Elmer Assistant, a undertaking on GitHub. It needs to be pretty simple to switch its immediate and supply information to your wants. Nonetheless, I haven’t had nice luck operating this domestically, though I’ve a reasonably sturdy Home windows PC.
Replace your likes by scheduling the script to run mechanically
As a way to be helpful, you’ll have to preserve the underlying “posts I’ve favored” knowledge updated. I run my script manually on my native machine periodically once I’m energetic on Bluesky, however you may as well schedule the script to run mechanically day-after-day or as soon as every week. Listed below are three choices:
- Run a script domestically. In the event you’re not too frightened about your script all the time operating on a precise schedule, instruments comparable to taskscheduleR for Home windows or cronR for Mac or Linux may also help you run your R scripts mechanically.
- Use GitHub Actions. Johannes Gruber, the creator of the atrrr bundle, describes how he uses free GitHub Actions to run his R Bloggers Bluesky bot. His directions might be modified for different R scripts.
- Run a script on a cloud server. Or you could possibly use an occasion on a public cloud comparable to Digital Ocean plus a cron job.
It’s your decision a model of your Bluesky likes knowledge that doesn’t embody each publish you’ve favored. Generally chances are you’ll click on like simply to acknowledge you noticed a publish, or to encourage the creator that individuals are studying, or since you discovered the publish amusing however in any other case don’t count on you’ll need to discover it once more.
Nonetheless, a warning: It might get onerous to manually mark bookmarks in a spreadsheet in case you like numerous posts, and you must be dedicated to maintain it updated. There’s nothing unsuitable with looking out by means of your complete database of likes as a substitute of curating a subset with “bookmarks.”
That stated, right here’s a model of the method I’ve been utilizing. For the preliminary setup, I recommend utilizing an Excel or .csv file.
Step 1. Import your likes right into a spreadsheet and add columns
I’ll begin by importing the my_likes.parquet file and including empty Bookmark and Notes columns, after which saving that to a brand new file.
my_likes
mutate(Notes = as.character(""), .earlier than = 1) |>
mutate(Bookmark = as.character(""), .after = Bookmark)
rio::export(likes_w_bookmarks, "likes_w_bookmarks.xlsx")
After some experimenting, I opted to have a Bookmark column as characters, the place I can add simply “T” or “F” in a spreadsheet, and never a logical TRUE or FALSE column. With characters, I don’t have to fret whether or not R’s Boolean fields will translate correctly if I resolve to make use of this knowledge outdoors of R. The Notes column lets me add textual content to elucidate why I would need to discover one thing once more.
Subsequent is the handbook a part of the method: marking which likes you need to preserve as bookmarks. Opening this in a spreadsheet is handy as a result of you possibly can click on and drag F or T down a number of cells at a time. If in case you have numerous likes already, this can be tedious! You may resolve to mark all of them “F” for now and begin bookmarking manually going ahead, which can be much less onerous.
Save the file manually again to likes_w_bookmarks.xlsx.
Step 2. Maintain your spreadsheet in sync along with your likes
After that preliminary setup, you’ll need to preserve the spreadsheet in sync with the information because it will get up to date. Right here’s one approach to implement that.
After updating the brand new deduped_my_likes likes file, create a bookmark test lookup, after which be a part of that along with your deduped likes file.
bookmark_check
choose(URL, Bookmark, Notes)
my_likes_w_bookmarks
relocate(Bookmark, Notes)
Now you could have a file with the brand new likes knowledge joined along with your current bookmarks knowledge, with entries on the high having no Bookmark or Notes entries but. Save that to your spreadsheet file.
rio::export(my_likes_w_bookmarks, "likes_w_bookmarks.xlsx")
A substitute for this considerably handbook and intensive course of could possibly be utilizing dplyr::filter() in your deduped likes knowledge body to take away objects you realize you gained’t need once more, comparable to posts mentioning a favourite sports activities staff or posts on sure dates when you realize you centered on a subject you don’t have to revisit.
Subsequent steps
Need to search your personal posts as nicely? You possibly can pull them by way of the Bluesky API in an identical workflow utilizing atrrr’s get_skeets_authored_by() perform. When you begin down this street, you’ll see there’s much more you are able to do. And also you’ll doubtless have firm amongst R customers.
